Charting Galactic Accelerations with Stellar Streams and Machine Learning

TL;DR

This paper introduces a neural network-based, data-driven method to reconstruct the galactic acceleration field from stellar streams without assuming a specific gravitational potential model beforehand.

Contribution

It presents a novel, flexible neural network approach that models stellar streams as collections of orbits with varying energies, enabling direct acceleration field estimation from phase-space data.

Findings

Successfully recovers parameters of a simulated triaxial halo potential.

Can constrain complex galactic potentials using stellar stream data.

Applicable to both simple and complicated gravitational models.

Abstract

We present a data-driven method for reconstructing the galactic acceleration field from phase-space measurements of stellar streams. Our approach is based on a flexible and differentiable fit to the stream in phase-space, enabling a direct estimate of the acceleration vector along the stream. Reconstruction of the local acceleration field can be applied independently to each of several streams, allowing us to sample the acceleration field due to the underlying galactic potential across a range of scales. Our approach is methodologically different from previous works, since a model for the gravitational potential does not need to be adopted beforehand. Instead, our flexible neural-network-based model treats the stream as a collection of orbits with a locally similar mixture of energies, rather than assuming that the stream delineates a single stellar orbit. Accordingly, our approach allows for distinct regions of the stream to have different mean energies, as is the case for real stellar streams. Once the acceleration vector is sampled along the stream, standard analytic models for the galactic potential can then be rapidly constrained. We find our method recovers the correct parameters for a ground-truth triaxial logarithmic halo potential when applied to simulated stellar streams. Alternatively, we demonstrate that a flexible potential can be constrained with a neural network, though standard multipole expansions can also be constrained. Our approach is applicable to simple and complicated gravitational potentials alike, and enables potential reconstruction from a fully data-driven standpoint using measurements of slowly phase-mixing tidal debris.